The present invention is related to the field of image reconstruction in digital x-ray tomography or tomosynthesis, and more particularly, to the reconstruction of three-dimensional (3D) tomographic images in digital tomography or tomosynthesis using a graphics processing unit (GPU).
Tomography is imaging by sections or sectioning an object into multiple images, and reconstructing the images to view an object of interest. The advantage of tomography over conventional x-ray imaging is that it eliminates the superimposition of images of structures and tissues outside the area of interest.
Today, projection mammography is considered the gold standard for the detection of breast cancer. However, both film-screen and digital mammography are subject to a number of fundamental limitations related to the projection process, whereby two-dimensional (2D) images are produced of the 3D breast anatomy. As a result, mammography superimposes normal tissues resulting in artifactual densities that often necessitate a biopsy; this leads to a loss in specificity. In addition, true lesions may be masked by the superimposed normal tissue and thereby rendered undetectable; this reduces the sensitivity of mammography.
Tomographic x-ray breast imaging would obviate these limitations. Preliminary studies of breast tomosynthesis have demonstrated a 16% increase in sensitivity and 85% decrease in false positives as compared to digital mammography. It is widely believed that digital breast tomosynthesis (DBT) has the potential to replace mammography (both digital and film) in the future, based on preliminary clinical results.
In Digital Tomosynthesis, a 3D tomographic image of an object is reconstructed from a limited set of 2D radiographic projection images. A digital tomosynthesis system includes one or more x-ray sources and one or more one-dimensional (1D) or 2D x-ray digital detectors. In the most common form of digital tomosynthesis, an x-ray source is rotated by a gantry in an arc through a limited range of angles about a pivot point. A set of projection radiographs of the object are acquired by the detector at discrete locations about the x-ray source. In other embodiments, the source may be held stationary while the detectors are moved, or the source and the detector may both move.
FIGS. 1A-1D illustrate various examples of image acquisition geometries. FIG. 1A shows the most common acquisition geometry. In this acquisition system, the x-ray focus (Fi) is positioned sequentially at multiple locations (F1, F2, F3, . . . ). At each location (Fi), a projection image of the acquired anatomy (in this case, the breast) is made onto the detector to produce an image (Di). A lesion (shown as a dot in plane R) will be projected to different locations on the detector. When backprojected, the various images will add coherently in the tomosynthesis image of plane R to reconstruct the lesion. For illustration, the x-ray foci (Fi) are shown in an arc with equal spacing; in fact, the location and spacing of the x-ray foci may be arbitrary. Similarly, the detector is shown to be held rigidly; again, this is only for the purposes of illustration; the detector may also be oriented in an arbitrary manner.
FIG. 1B shows a second acquisition geometry. In this example, detector (D) rotates as the x-ray focus (Fi) moves through a limited range of angles. A lesion (again shown as a dot in plane R) will be projected to different locations on the detector. When backprojected, the various images will add coherently in the tomosynthesis image of the plane R to reconstruct the lesion. In FIG. 1B, the x-ray detector is shown perpendicular to the central axis of the x-ray beam. However, the angle of the x-ray detector to the central axis of the x-ray beam can be arbitrary. Also, the spacing and location of the x-ray foci F can be arbitrary. This geometry is similar to that described in Patent Application Publication No. US 2007/0036265.
FIG. 1C shows a third example acquisition geometry. In this example, both the x-ray source (F) and a set of linear detectors (Di) move continuously along an axis. The system includes a pre-collimator (P) to define the x-ray beam as it passes through the patient. Each line detector records a linear image of radiation as transmitted through the anatomy at a unique angle and position. A plurality of 2D images may be formed, where each 2D image is formed from a plurality of line images as recorded by a single one of the line detectors. Note that only 5 linear detectors are shown in FIG. 1C for clarity. This geometry is similar to that described in U.S. Pat. No. 6,940,942.
FIG. 1D shows a fourth example acquisition geometry. In this example, the x-ray focus is moved continuously while a small number of discrete linear detectors produce a sequential set of linear images of the anatomy. Note that only a small number of acquisition locations are shown for clarity. This geometry is similar to that described in Patent Application Publication No. US 2007/0098141.
Regardless of acquisition geometry used, after the projection images are acquired, they are reconstructed into a set of 3D tomographic images that are saved and then reviewed at a later time.
FIG. 2A illustrates the prior art technique for viewing images. In step 202, all 3D tomographic images are reconstructed at fixed increments (e.g., 1 mm for tomosynthesis to 3 mm for CT). Once a 3D image dataset is reconstructed, the images are saved at step 204. Once saved, the process proceeds to step 206 whereby a user (e.g., a radiologist) views the images at a later time. If the user wishes to change any of the reconstruction parameters used to process the images, the process may begin again and a new set of 3D tomographic images must be reconstructed. This reconstruction method is essentially an off-line approach.
After the projection images are acquired, they must be reconstructed into 3D tomographic images. Iterative tomosynthesis reconstruction methods, such as algebraic reconstruction techniques and maximum likelihood estimation maximization, generally involve reconstructing a 3D) tomographic image of the full imaged volume. These techniques provide good image quality but are computationally expensive as they generally involve an initial reconstruction followed by iterative updates of the full 3D image dataset until a threshold criterion is met. In DBT, typical reconstruction times vary from 5 to 30 minutes to reconstruct one 3D image dataset per breast.
Single-pass reconstruction techniques used in tomosynthesis do not generally require a reconstruction of the full 3D image dataset and may thus be computationally efficient. Single-pass reconstruction allows reconstruction of a 3D tomographic image in a single iteration consisting of a set of reconstruction steps. In this example, a set of reconstruction steps is defined as the performance of steps 202 and 204 as these steps may be iteratively performed numerous times, each time resulting in the reconstruction of a single image. Examples of single-pass reconstruction techniques are shift-and-add algorithms and simple backprojection in which 2D projection images are spatially translated with respect to each other through the image plane (hence the name “backprojection”) to obtain a rough approximation of the original. The projections interact constructively in regions that correspond to the structures in the original object. Structures not in the reconstructed image plane are blurred. Filtered backprojection (FBP) is another backprojection method in which the projection images are filtered prior to reconstruction.
Each of these conventional reconstruction techniques has inherent drawbacks, e.g., poor image quality, lengthy computational time, extensive required filtering. By maintaining a large number of image files for reconstruction, each technique is also highly demanding on a central processing unit (CPU), thereby causing additional processing delays. Several approaches have been attempted to overcome these drawbacks, such as application specific integrated circuits (ASICs) of specifically designed field programmable gate arrays (FPGAs), which is a device that can be configured to perform a specific task. Both these approaches are expensive though, as the ASICs or FPGAs are designed for a single, specific purpose and do not provide much, if any, additional scalability.
Graphic Processor Units (GPUs) are pipeline processors, designed to accelerate the graphics rendering pipeline. Many of the gains in GPU performance have arisen from the ability to parallelize the various elements of the pipeline. Traditionally, the GPU architecture included vertex processors, called vertex shaders, which were specialized for geometric computations, and pixel processors or pixel shaders which were specialized for point operations. More recently, GPUs have been based upon a unified shader architecture in which unified processors will switch between the two types of shaders depending on the work that needs to be done.
Graphics objects are typically composed of polygon meshes, where additional surface detail can be modeled by mapping images or textures onto the polygons during the rendering phase. Texture mapping is a technique for efficiently modeling a surface's properties and is an efficient way to provide intricate surface detail without increasing an object's polygon count. GPUs are highly optimized to perform texture mapping very quickly, even under perspective distortion.
Modern GPUs are organized in a manner similar to that of GPU 300 shown in FIG. 3. The input to the pipeline is a list of geometric objects specified by vertices and temporarily stored in a vertex buffer 302. A vertex is the point intersection of two sides of a polygon. The vertices are transformed to the object's position in 3D space and projected to the screen plane by vertex shaders 304. The projected vertices are then assembled into triangles in the screen space and sent to the rasterizer 306. Rasterizer 306 produces zero or more fragments, one fragment for each pixel covered by the triangle. One or more pixel shaders, or in this example fragment processor 308, calculates the color (or shade of gray) for each fragment, typically using values from the texture memory 312. Finally, for each fragment, a pixel is written to the frame buffer 310 or back to texture memory 312.
With recent advances in GPU performance and architecture, GPUs are now increasingly being used as cost-effective high-performance co-processors for scientific computing and medical imaging. Primarily designed to deliver ultra-high definition graphics for video games in real-time. GPU performance has been increasing at a rate triple to Moore's Law in the last 10 years and is currently over an order of magnitude faster than that of CPUs. In addition, new generation GPUs offer programmability at floating point precision. For these reasons. GPUs are increasingly being used as cost-effective high-performance co-processors for scientific computing and medical imaging.